What Is ChatGPT’s Training Data and How Do You Know if Your Brand Is in It?

ChatGPT typically generates answers using what it learned during training. In some versions, it can also pull in live information through tools like web browsing, but most responses rely on its internal dataset. That training data was collected before the model’s release, and it shapes what ChatGPT “knows” by default.

If your brand is part of that training data, it can appear in answers even without a real-time lookup. If it isn’t, ChatGPT may skip over it or rely on vague or outdated information. Being in the training data gives your brand lasting visibility inside the model itself, not just on the live web.

In this post, I want to focus on that. How training data works, where it comes from, and what it means if your brand isn’t part of it.

What We Know About GPT‑3 Training Data

OpenAI released details about the GPT-3 training dataset in a technical paper. While they didn’t list every individual source, they shared the breakdown of the major components.

Here are the major components of GPT-3's data:

Common Crawl: A massive archive of web pages collected over many years. The raw Common Crawl dataset is messy, so OpenAI and other model developers filter it heavily before using it in training.

WebText2: A curated set of high-quality web content originally extracted from outbound links posted on Reddit. This was filtered to include only links with more than 3 upvotes to ensure quality.

Books1 and Books2: These are large collections of books, likely drawn from public domain and licensed sources.

Wikipedia: The public encyclopedia.

The table below summarizes how those sources contributed. It shows the estimated size of each dataset, how much weight it had in training, and how many times the model saw each token on average.

The table has three key columns: Estimated Tokens, Weight in Training, and Epochs.

• The Estimated Tokens column shows how much raw content was used from each source, measured in billions of tokens.
• The Weight in Training reflects how much of the overall training data each dataset contributed. It’s not the same as the dataset size, but it reflects its relative importance during the learning process.
• The Epochs column shows how many times the model went through each dataset on average, assuming a total of 300 billion training tokens.

Together, these columns explain how OpenAI balanced dataset size with repetition. Some smaller datasets were repeated more often to increase their impact, while larger ones like Common Crawl were used less frequently per token but still made up the bulk of the training due to sheer size.

The Weight in Training column is the best indicator of how much influence each dataset had on the final model.

What We Know About GPT‑4, GPT‑4.5, and GPT‑5

OpenAI hasn’t shared a breakdown like the one above for GPT‑4, GPT-4.5, or the soon-to-come GPT‑5, but there are some things we can reasonably assume based on GPT-3, public statements, and consistent patterns. They’ve said GPT‑4 was trained on a mix of publicly available and licensed data, which almost certainly still includes filtered Common Crawl, Wikipedia, books, and curated web text. What’s new is the addition of licensed sources. OpenAI has been pursuing data partnerships with platforms like Stack Overflow along with large publishers and academic providers. While it's not confirmed those datasets were used in training, it’s a reasonable assumption based on timing and priorities.

GPT‑4.5, released in February 2025, is the latest version available in ChatGPT for Pro users. OpenAI has described it as a scaled-up continuation of the GPT‑4 approach, trained with more compute, a broader data mix, and newer optimization techniques. While no specific dataset list is available, its training likely relied on the same core components as GPT‑4, with added emphasis on licensed, high-quality sources.

As for GPT‑5, OpenAI hasn’t shared concrete details pre-release, but based on timing and past patterns, it’s expected to follow a similar structure: large-scale public web data, selective filtering, and content partnerships.

What This Means For Your Brand

If your brand wasn’t in the public training data before 2023, it likely isn't part of ChatGPT’s memory by default. That means that, without relying on browsing, it won’t be able to recall basic facts about you, mention your products, or include you in answers that rely purely on what the model already knows. You’re not necessarily invisible, but you're not baked in.

This makes a strong case for thinking about visibility on two tracks: past presence and future retrieval.

How to Check If You’re Likely in ChatGPT’s Training Data

There’s no master list of what went into recent models, but you can still make a solid educated guess. Here’s a simple way to check if your content was part of the public data most models rely on.

Step 1: Test What ChatGPT Knows Without Browsing

As of when I am writing this article, there is no switch to disable browsing manually, but you can still prompt ChatGPT in a way that limits its use of real-time data.

How to check:

1. Start a new chat in ChatGPT:
2. Ask a question like:
   “Without browsing or looking anything up, what do you know about [Brand Name]?”
3. If the model gives you vague, outdated, or clearly incorrect info, or says it doesn't know, your brand likely isn’t in the training data.

Note: This isn't a guaranteed method. A model could still hallucinate familiarity or fabricate answers.

Step 2: Search Common Crawl

Common Crawl was a major data source for GPT‑3 and almost certainly plays a role in newer models.

How to check:

1. Go to https://index.commoncrawl.org
2. Click on a crawl from before 2023 (for example, anything from 2022 or earlier)
3. In the search bar, enter your domain or specific URL, like:
   yourdomain.com
   or
   https://www.yourdomain.com/page-name
4. If no results show up, your content likely wasn’t captured in the datasets used for training

Step 3: Check Wikipedia and Wikidata

Wikipedia and Wikidata are treated as high-confidence sources and are oversampled in training. If your brand has an entry, there’s a good chance it's known to the model.

How to check:

1. Search https://www.wikipedia.org for your brand
2. Then check https://www.wikidata.org using your brand name

Step 4: Check for 3rd-Party Mentions on Crawlable Sites

You don’t have to publish the content yourself. If your brand is mentioned on multiple reputable, publicly crawlable websites, like news sites, blogs, directories, or review platforms, there’s a better chance those mentions made it into the training data.

How to check:

Option 1: Use a brand mention monitoring tool

Tools like Semrush Brand Monitoring or Ahrefs Alerts can help you find brand mentions across the web. These tools surface citations of your brand on indexed, crawlable pages

Option 2: Use Google’s search operators

If you don’t have access to a tool, you can use Google to manually spot older mentions. Try queries like:

"Your Brand Name" before:2023

1. Search for exact matches of your brand name (because it’s in quotes)
2. Only show pages that were published or indexed before January 1, 2023

"Your Brand Name" site:example.com

1. Look for exact matches of your brand name
2. Only within a specific domain, like example.com. If you're checking whether a specific site mentioned your brand (such as a news site, blog, or partner), this helps confirm whether that mention happened on a publicly accessible page.

Step 5: Look for Reddit Links

WebText2, one of the key datasets in GPT-3, was built by collecting outbound links from Reddit. If people were sharing your content there before 2023, there’s a good chance it was included in that set.

How to check:

Option 1: Use Google’s site: operator

• Search: site:reddit.com yourdomain.com
  This shows Reddit posts that linked directly to your site. Look for links with more than 3 upvotes.
• Add before:2023 to focus on links posted before GPT-3 was trained.

What To Do If You’re Not in the Training Data

If your brand isn’t showing up in ChatGPT responses and you didn’t find signs of inclusion in Google, Common Crawl, Wikipedia, Reddit, or other major sources, it’s likely not part of the current training data. That doesn’t mean you can’t be visible. It just means you’ll need to focus on getting crawled in real-time, and start preparing for the next training update.

Here’s what you need to do:

1. Publish valuable, crawlable content
Focus on original, helpful pages that attract links and get shared. Avoid gated or duplicate content.

2. Earn mentions on other sites
Get cited by news outlets, blogs, or partners. Third-party mentions boost visibility in Common Crawl and other datasets.

3. Build a presence on Reddit
Reddit-linked content shaped WebText2. Be active in relevant subreddits and contribute in ways that earn upvotes and discussion. Focus on building a community instead of spamming.

4. Get on Wikipedia (if you qualify)
Wikipedia and Wikidata are heavily used in training. If your brand meets notability standards, aim to get listed.

5. Strengthen your brand presence
The more people talk about you across public, indexed sites, the more likely you are to be included in future models.

Want to learn more strategies? Read my article on 11 data-backed strategies to improve visibility in AI search.